While there is some variance depending upon the state of the economy and market conditions in general, the volume of securities traded on various worldwide markets and exchanges is exceedingly large and getting larger. Securities in this context can be any of a number of financial instruments such as stocks, bonds, mortgage backed securities, options, or, alternatively, hard assets such as precious metals, commodities and the like. The common element among these widely traded vehicles (hereinafter collectively referred to as “securities” for ease of reference), however, is that they enjoy a great deal of liquidity and the markets in which they trade are well established with many different buyers and sellers who participate in buying and selling the applicable security.
When there are a large number of buyers and sellers, the market for that security tends to be more active and perhaps more importantly, the spread between the available purchase price (the “ask”) and the available selling price (the “bid”) tends to be narrower. This in turn encourages buyers and sellers to participate in the buying and selling of that security since they are less likely to overpay or sell for too low of a price solely because of the “transaction cost” associated with the buy/sell spread which is incurred in executing the buy or sell transaction. Additionally, markets for securities tend to be more active where commissions and/or other fees and charges associated with the purchase and sale transaction are lower since the collective costs incurred from such costs and the bid/ask spread directly impact the profitability of trading that security.
In addition to minimizing transaction costs, profitable trading necessarily involves the need to purchase securities at a lower cost than the price at which the security is ultimately sold. Or, in the case of short selling, it is necessary to first sell the security at a higher cost than the price at which the security is ultimately covered. There are various known techniques, systems and methodologies for attempting to do just this. For example, some traders (typically individuals or “retail” traders as opposed to professional or “institutional” traders) will trade manually, largely based on nothing more than a gut feel. Alternatively, various individuals and even sophisticated individuals and institutional traders will use manual “systems” under which they devise a plan to make specific trades under various circumstances and market conditions. For example, such a trading plan may be as simple as buying XYZ stock when it sells for a price of $40 or lower (ask at $40 or below) and selling that same stock which it sells for a price of $44 or above (bid at $44 or above).
The foregoing plan may be implemented as a simple trading policy that a trader manually follows by entering appropriate buy and sell orders at the appropriate times. Or, the trader may utilize an online broker that provides the functionality for the trader to enter standing orders to make these trades when the specified market conditions are met. As yet another example, the trader may employ a software based tool that interacts and communicates with his or her brokerage trading platform to execute trades consistent with trading system rules. Other applications and services are also available which offer traders the ability to implement their own trading plan and/or plans and strategies developed by third parties.
The trading plan described above is generally considered to fall within the class of trading methodologies referred to as “technical analysis”. In this class of trading methodologies, specific decisions are made based solely on historic price movement for the underlying security as well as expected future price movement based on mathematical analysis tied to price/time chart movements. Technical analysis techniques for predicting and acting upon expected future price movement are in widespread use by retail and institutional traders.
This class of techniques and the systems that implement them, however, do suffer from a number of drawbacks. For example, in many cases, a great many competing traders are using the same systems with the same predictive algorithms and are acting upon these predictions generated by these systems at the same time. At a market based level, this produces undesirable outcomes for these traders since they are competing at the same time to buy a security with others using the same algorithms based on the same predictions at the same time. Further, they are also competing against each other when the system indicates that the trader should sell a security. In both of these cases, an artificial demand (buy signal) or supply (sell signal) is created which tends to move the price up or down, respectively beyond what it would otherwise be and thus resulting, in theory, in a less profitable trade for each of the traders using the same system.
Another disadvantage of technical analysis is that, by definition, it is based on price movement that has occurred in the past and this information is used to predict price movement for the future. Unfortunately, it is theorized that price movement is largely random and instead driven only by supply and demand which exists in real time as opposed to what has happened in the past. The net result of this is that technical analysis tools, while they can be useful, are often times not the ideal predictor of future price movement.
Another class of trading techniques which are in use are those known as “fundamental analysis”. This class of techniques relies on examining the fundamental properties of the asset underlying the security. For example, for a common stock associated with a company, trading decisions may be based on earnings, revenue and/or newsworthy events about that company's positioning within its industry. A practically unlimited number of other metrics may be used as well. More common examples include price to earnings ratios, level of debt, earnings growth, deals expected to add to revenue in the future, etc. In the case of securities which represent ownership in hard assets such as gold, oil, etc., trading decisions using fundamental analysis might include such metrics as predicted demand for the underlying asset, predicted supply, newsworthy stories regarding the applicable asset such as new oil wells being drilled, disruptions in the supply chain for bringing the asset to the end user, etc.
While fundamental analysis based decisions and the systems that implement them also have their place in trading, they also suffer from drawbacks. For example, notwithstanding a very good understanding of a company and its financial picture, the market for stock representing ownership in that company may depart from the realities of the value of that company. This is evidenced by the fact that all stocks do not, for example, trade at the same multiple of earnings. There are other factors that go into the real time price for a stock that can not be addressed by fundamental analysis. Examples include “buzz” about certain companies and industries, rumors concerning that company, and other intangible aspects of the value of a particular stock that can not be measured or predicted using known fundamental analysis techniques.